Imagen de Portada

Distilling Normalizing Flows

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Walton, Steven, Klyukin, Valeriy, Artemev, Maksim, Derkach, Denis, Orlov, Nikita, Shi, Humphrey
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Evaluating Deepfake Detectors in the Wild
por: Pirogov, Viacheslav, et al.
Publicado: (2025)

Ensemble Distributionally Robust Bayesian Optimisation
por: Ramazyan, Tigran, et al.
Publicado: (2026)

Efficient Image Generation with Variadic Attention Heads
por: Walton, Steven, et al.
Publicado: (2022)

NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows
por: Tarasov, Denis, et al.
Publicado: (2025)

Online Neural Networks for Change-Point Detection
por: Hushchyn, Mikhail, et al.
Publicado: (2020)

FastWave: Optimized Diffusion Model for Audio Super-Resolution
por: Kuznetsov, Nikita, et al.
Publicado: (2026)

The Coupling Within: Flow Matching via Distilled Normalizing Flows
por: Berthelot, David, et al.
Publicado: (2026)

Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
por: Ali, Saraa, et al.
Publicado: (2025)

CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices
por: Znobishchev, Andrei, et al.
Publicado: (2024)

Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space
por: Ramazyan, Tigran, et al.
Publicado: (2024)

Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors
por: Svirin, Stepan, et al.
Publicado: (2024)

Abstractive summarization from Audio Transcription
por: Derkach, Ilia
Publicado: (2024)

Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
por: Ibrahim, Anwar, et al.
Publicado: (2025)

Preferential Normalizing Flows
por: Mikkola, Petrus, et al.
Publicado: (2024)

Piecewise Normalizing Flows
por: Bevins, Harry, et al.
Publicado: (2023)

Who to Trust? Aggregating Client Predictions in Federated Distillation
por: Kovalchuk, Viktor, et al.
Publicado: (2025)

Smaller, Faster, Cheaper: Architectural Designs for Efficient Machine Learning
por: Walton, Steven
Publicado: (2025)

RL-ABC: Reinforcement Learning for Accelerator Beamline Control
por: Ibrahim, Anwar, et al.
Publicado: (2026)

Principled Interpolation in Normalizing Flows
por: Fadel, Samuel G., et al.
Publicado: (2020)

Flexible Tails for Normalizing Flows
por: Hickling, Tennessee, et al.
Publicado: (2024)

Causally Consistent Normalizing Flow
por: Zhou, Qingyang, et al.
Publicado: (2024)

Gradient-Normalized Smoothness for Optimization with Approximate Hessians
por: Semenov, Andrei, et al.
Publicado: (2025)

Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)
por: Kornilov, Nikita, et al.
Publicado: (2025)

Normalizing Flows are Capable Models for RL
por: Ghugare, Raj, et al.
Publicado: (2025)

Normalizing Flows for Conformal Regression
por: Colombo, Nicolo
Publicado: (2024)

DR-SNE: Density-Regularized Stochastic Neighbor Embedding
por: Kazanskii, Maksim
Publicado: (2026)

Free-form Flows: Make Any Architecture a Normalizing Flow
por: Draxler, Felix, et al.
Publicado: (2023)

IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
por: Fan, Wei, et al.
Publicado: (2024)

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
por: Yang, Chenyu, et al.
Publicado: (2026)

Multivariate Latent Recalibration for Conditional Normalizing Flows
por: Dheur, Victor, et al.
Publicado: (2025)

Gradual Domain Adaptation via Normalizing Flows
por: Sagawa, Shogo, et al.
Publicado: (2022)

FlowDistill: Scalable Traffic Flow Prediction via Distillation from LLMs
por: Yu, Chenyang, et al.
Publicado: (2025)

FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation
por: Lee, Chang Won, et al.
Publicado: (2024)

Tight Bounds for Schrödinger Potential Estimation in Unpaired Data Translation
por: Puchkin, Nikita, et al.
Publicado: (2025)

Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
por: Cabezas, Alberto, et al.
Publicado: (2024)

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
por: Chen, Fan, et al.
Publicado: (2026)

On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
por: Draxler, Felix, et al.
Publicado: (2024)

Fast and Unified Path Gradient Estimators for Normalizing Flows
por: Vaitl, Lorenz, et al.
Publicado: (2024)

FlowVAT: Normalizing Flow Variational Inference with Affine-Invariant Tempering
por: Qin, Juehang, et al.
Publicado: (2025)

FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
por: Dong, Yuexiao, et al.
Publicado: (2026)